{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sweeps - Capacitance matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prerequisite\n",
    "You need to have a working local installation of Ansys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Perform the necessary imports and create a QDesign in Metal first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit_metal as metal\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings\n",
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "from qiskit_metal.renderers.renderer_ansys.ansys_renderer import QAnsysRenderer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "design = designs.DesignPlanar()\n",
    "gui = MetalGUI(design)\n",
    "\n",
    "from qiskit_metal.qlibrary.qubits.transmon_pocket import TransmonPocket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.variables['cpw_width'] = '15 um'\n",
    "design.variables['cpw_gap'] = '9 um'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this example, the design consists of 4 qubits and 4 CPWs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Allow running the same cell here multiple times to overwrite changes\n",
    "design.overwrite_enabled = True\n",
    "\n",
    "## Custom options for all the transmons\n",
    "options = dict(\n",
    "    # Some options we want to modify from the defaults\n",
    "    # (see below for defaults)\n",
    "    pad_width = '425 um', \n",
    "    pocket_height = '650um',\n",
    "    # Adding 4 connectors (see below for defaults)\n",
    "    connection_pads=dict(\n",
    "        readout = dict(loc_W=+1,loc_H=-1, pad_width='200um'),\n",
    "        bus1 = dict(loc_W=-1,loc_H=+1, pad_height='30um'),\n",
    "        bus2 = dict(loc_W=-1,loc_H=-1, pad_height='50um')\n",
    "    )\n",
    ")\n",
    "\n",
    "## Create 4 transmons\n",
    "\n",
    "q1 = TransmonPocket(design, 'Q1', options = dict(\n",
    "    pos_x='+2.42251mm', pos_y='+0.0mm', **options))\n",
    "q2 = TransmonPocket(design, 'Q2', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='-0.95mm', orientation = '270', **options))\n",
    "q3 = TransmonPocket(design, 'Q3', options = dict(\n",
    "    pos_x='-2.42251mm', pos_y='+0.0mm', orientation = '180', **options))\n",
    "q4 = TransmonPocket(design, 'Q4', options = dict(\n",
    "    pos_x='+0.0mm', pos_y='+0.95mm', orientation = '90', **options))\n",
    "\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "RouteMeander.get_template_options(design)\n",
    "\n",
    "options = Dict(\n",
    "        lead=Dict(\n",
    "            start_straight='0.2mm',\n",
    "            end_straight='0.2mm'),\n",
    "        trace_gap='9um',\n",
    "        trace_width='15um')\n",
    "\n",
    "def connect(component_name: str, component1: str, pin1: str, component2: str, pin2: str,\n",
    "            length: str, asymmetry='0 um', flip=False, fillet='90um'):\n",
    "    \"\"\"Connect two pins with a CPW.\"\"\"\n",
    "    myoptions = Dict(\n",
    "        fillet=fillet,\n",
    "        hfss_wire_bonds = True,\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(\n",
    "                component=component1,\n",
    "                pin=pin1),\n",
    "            end_pin=Dict(\n",
    "                component=component2,\n",
    "                pin=pin2)),\n",
    "        total_length=length)\n",
    "    myoptions.update(options)\n",
    "    myoptions.meander.asymmetry = asymmetry\n",
    "    myoptions.meander.lead_direction_inverted = 'true' if flip else 'false'\n",
    "    return RouteMeander(design, component_name, myoptions)\n",
    "\n",
    "asym = 140\n",
    "cpw1 = connect('cpw1', 'Q1', 'bus2', 'Q2', 'bus1', '5.6 mm', f'+{asym}um')\n",
    "cpw2 = connect('cpw2', 'Q3', 'bus1', 'Q2', 'bus2', '5.7 mm', f'-{asym}um', flip=True)\n",
    "cpw3 = connect('cpw3', 'Q3', 'bus2', 'Q4', 'bus1', '5.6 mm', f'+{asym}um')\n",
    "cpw4 = connect('cpw4', 'Q1', 'bus1', 'Q4', 'bus2', '5.7 mm', f'-{asym}um', flip=True)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Metal passes information to the simulator \"q3d\" to extract the capacitance matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = LOManalysis(design, \"q3d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare data to pass as arguments for method run_sweep()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "render_design_argument_qcomps = ['Q1']\n",
    "\n",
    "render_design_argument_endcaps = [('Q1', 'readout'), ('Q1', 'bus1'),('Q1', 'bus2')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Setup',\n",
       " 'reuse_selected_design': True,\n",
       " 'reuse_setup': True,\n",
       " 'freq_ghz': 5.0,\n",
       " 'save_fields': False,\n",
       " 'enabled': True,\n",
       " 'max_passes': 15,\n",
       " 'min_passes': 2,\n",
       " 'min_converged_passes': 2,\n",
       " 'percent_error': 0.5,\n",
       " 'percent_refinement': 30,\n",
       " 'auto_increase_solution_order': True,\n",
       " 'solution_order': 'High',\n",
       " 'solver_type': 'Iterative'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To identify the agruments that you can change.\n",
    "# They will change based on the simulation software used.\n",
    "c1.sim.setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the simulation software, if you don't want to use the default values, \n",
    "# you can update them as seen below.\n",
    "\n",
    "# If a setup named \"sweeper_q3d_setup\" exists in the project, it will be deleted, \n",
    "# and a new setup will be added.\n",
    "c1.sim.setup.name = \"sweeper_q3d_setup\"\n",
    "\n",
    "c1.sim.setup.freq_ghz = 5.6\n",
    "c1.sim.setup.max_passes = 9\n",
    "c1.sim.setup.min_passes = 2\n",
    "c1.sim.setup.percent_error = 0.45"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We will look at modifying the pad_gap of qubit 1, to see how it impacts the anharmonicity of the qubit.\n",
    "\n",
    "The steps will be;\n",
    "- Connect to Ansys Q3D.\n",
    "- Rebuild QComponents in Metal.\n",
    "- Render QComponents within Q3D and setup the simulation.\n",
    "- Delete/Clear the Q3D between each simulation.\n",
    "- Using the capacitance matrices, LOM for each value in option_sweep is found.\n",
    "\n",
    "#### Returns a dict and return code.  If the return code is zero, there were no errors detected.  \n",
    "#### The dict has:  key = each value used to sweep, value = capacitance matrix\n",
    "\n",
    "#### This could take minutes or hours based on the complexity of the design.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:08AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 08:08AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 08:08AM [load_ansys_project]: \tOpened Ansys Desktop v2020.2.0\n",
      "INFO 08:08AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Ansoft/\n",
      "\tProject:   Project23\n",
      "INFO 08:08AM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 08:08AM [connect]: \t Connected to project \"Project23\". No design detected\n",
      "INFO 08:08AM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
      "WARNING 08:08AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 08:08AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 08:08AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:08AM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:08AM [analyze]: Analyzing setup sweeper_q3d_setup\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpqt5sgbte.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp5ks6ofb0.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpxblcplxt.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpuhodx2js.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp70ngvsc6.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpum8bsgyx.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpc82acfxx.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpkuctcf1q.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmptslx7ez1.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpmom37xqf.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.266513 [GHz]\n",
      "EC 284.490474 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -328.027635 [MHz]\n",
      "dispersion 16.258044 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 68.087440 [fF]\n",
      "T1 53.831750 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 7.457921 [fF]\n",
      "gbus1_in_MHz 108.061520 [MHz]\n",
      "χ_bus1 -2.196070 [MHz]\n",
      "1/T1bus1 2005.686739 [Hz]\n",
      "T1bus1 79.351845 [us]\n",
      "\n",
      "tCqbus2 -6.588291 [fF]\n",
      "gbus2_in_MHz -82.021155 [MHz]\n",
      "χ_bus2 -5.704388 [MHz]\n",
      "1/T1bus2 655.760880 [Hz]\n",
      "T1bus2 242.702711 [us]\n",
      "\n",
      "tCqbus3 5.453079 [fF]\n",
      "gbus3_in_MHz 70.106609 [MHz]\n",
      "χ_bus3 -2.763400 [MHz]\n",
      "1/T1bus3 295.077910 [Hz]\n",
      "T1bus3 539.365834 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 7.422809 [MHz]\n",
      "gbus1_3 10.345326 [MHz]\n",
      "gbus2_3 5.654427 [MHz]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:09AM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
      "INFO 08:09AM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:09AM [analyze]: Analyzing setup sweeper_q3d_setup\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpwe7jn05r.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpcrkv_5jk.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpaf2kqdlm.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpfxlsiiss.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp0p60le17.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpqlpr1t42.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpcmz4ifz4.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp_crzbc6u.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmptqdktbym.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
      "INFO 08:09AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpzrm77gi5.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.462897 [GHz]\n",
      "EC 307.600806 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -357.287459 [MHz]\n",
      "dispersion 33.474349 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 62.971968 [fF]\n",
      "T1 31.181639 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 7.545643 [fF]\n",
      "gbus1_in_MHz 116.213396 [MHz]\n",
      "χ_bus1 -3.378233 [MHz]\n",
      "1/T1bus1 3075.191676 [Hz]\n",
      "T1bus1 51.754479 [us]\n",
      "\n",
      "tCqbus2 -6.637477 [fF]\n",
      "gbus2_in_MHz -87.835128 [MHz]\n",
      "χ_bus2 -11.519528 [MHz]\n",
      "1/T1bus2 1457.770105 [Hz]\n",
      "T1bus2 109.176984 [us]\n",
      "\n",
      "tCqbus3 5.526346 [fF]\n",
      "gbus3_in_MHz 75.520647 [MHz]\n",
      "χ_bus3 -5.083082 [MHz]\n",
      "1/T1bus3 571.162003 [Hz]\n",
      "T1bus3 278.651140 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 7.279458 [MHz]\n",
      "gbus1_3 10.197376 [MHz]\n",
      "gbus2_3 5.507283 [MHz]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 08:09AM [connect_design]: \tOpened active design\n",
      "\tDesign:    GetCapacitance_q3d [Solution type: Q3D]\n",
      "INFO 08:09AM [get_setup]: \tOpened setup `sweeper_q3d_setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 08:09AM [analyze]: Analyzing setup sweeper_q3d_setup\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpa_vi_96w.txt, C, , sweeper_q3d_setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp67kj69wt.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 1, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpdr77b6cg.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 2, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp7bokcgn7.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 3, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmp8hut452g.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 4, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpycxt1u0b.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 5, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpvjp_qeb4.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 6, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmpfph35ir7.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 7, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmphwbh3l79.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 8, False\n",
      "INFO 08:10AM [get_matrix]: Exporting matrix data to (C:\\Temp\\tmparw9bhfb.txt, C, , sweeper_q3d_setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5600000000, Maxwell, 9, False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4] [5 0 1]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.603196 [GHz]\n",
      "EC 324.747859 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -379.244809 [MHz]\n",
      "dispersion 54.335718 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 59.646977 [fF]\n",
      "T1 19.153152 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 7.638504 [fF]\n",
      "gbus1_in_MHz 122.751924 [MHz]\n",
      "χ_bus1 -4.681162 [MHz]\n",
      "1/T1bus1 4274.193323 [Hz]\n",
      "T1bus1 37.236253 [us]\n",
      "\n",
      "tCqbus2 -6.694974 [fF]\n",
      "gbus2_in_MHz -92.444073 [MHz]\n",
      "χ_bus2 -21.099340 [MHz]\n",
      "1/T1bus2 3037.626181 [Hz]\n",
      "T1bus2 52.394513 [us]\n",
      "\n",
      "tCqbus3 5.592313 [fF]\n",
      "gbus3_in_MHz 79.741791 [MHz]\n",
      "χ_bus3 -8.315547 [MHz]\n",
      "1/T1bus3 997.775951 [Hz]\n",
      "T1bus3 159.509701 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 7.162962 [MHz]\n",
      "gbus1_3 10.056266 [MHz]\n",
      "gbus2_3 5.376093 [MHz]\n"
     ]
    }
   ],
   "source": [
    "sweep_data, return_code = c1.run_sweep(q1.name,\n",
    "                                       'pad_gap',\n",
    "                                       ['20um', '30um', '40um'],\n",
    "                                       render_design_argument_qcomps,\n",
    "                                       render_design_argument_endcaps,\n",
    "                                       design_name=\"GetCapacitance\",\n",
    "                                       box_plus_buffer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sweep Value</th>\n",
       "      <th>Ec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20um</td>\n",
       "      <td>284.490474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30um</td>\n",
       "      <td>307.600806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40um</td>\n",
       "      <td>324.747859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sweep Value          Ec\n",
       "0        20um  284.490474\n",
       "1        30um  307.600806\n",
       "2        40um  324.747859"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "ec_val = []\n",
    "for opt_val in sweep_data.keys():\n",
    "    ec_val.append([opt_val,sweep_data[opt_val]['variables']['lumped_oscillator']['EC']])\n",
    "\n",
    "df=DataFrame(ec_val,columns = ['Sweep Value', 'Ec'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can grab specific values from the results as seen below;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['20um', '30um', '40um'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['option_name', 'variables', 'sim_variables'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For each value of option, there is a set of data.\n",
    "sweep_data['20um'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['lumped_oscillator', 'lumped_oscillator_all'])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_data['20um']['variables'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['sim_setup_name', 'cap_matrix', 'units', 'cap_all_passes'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_data['20um']['sim_variables'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['20um', '30um', '40um']) \n",
      "\n",
      "\n",
      "key=20um\n",
      "option_name['20um']['option_name']=pad_gap\n",
      "variables={'lumped_oscillator': {'fQ': 5.266513054956589, 'EC': 284.4904739015804, 'EJ': 13.616300010297985, 'alpha': -328.02763545950194, 'dispersion': 16.25804359817505, 'gbus': array([108.06151986, -82.02115476,  70.10660922]), 'chi_in_MHz': array([-2.19607046, -5.70438759, -2.76340023])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha dispersion  \\\n",
      "1    5.6945  336.195588  13.6163 -394.022958  73.505735   \n",
      "2  5.597383  324.026735  13.6163 -378.317097  53.282182   \n",
      "3  5.486697   310.47202  13.6163 -360.949348  36.405901   \n",
      "4  5.417928  302.217736  13.6163 -350.437946  28.505987   \n",
      "5  5.355959  294.888507  13.6163 -341.145422  22.741803   \n",
      "6  5.311963  289.747388  13.6163 -334.649978  19.308807   \n",
      "7  5.289385  287.129029  13.6163 -331.349078   17.73438   \n",
      "8  5.266513  284.490474  13.6163 -328.027635  16.258044   \n",
      "\n",
      "                                                gbus  \\\n",
      "1  [102.84576293087251, -71.14320976656029, 74.94...   \n",
      "2  [107.77093095701179, -80.12086229266069, 65.69...   \n",
      "3  [105.54815295436816, -80.44038588561413, 67.29...   \n",
      "4  [104.90594389814552, -79.89581633369512, 68.00...   \n",
      "5  [106.33897608793431, -81.08798197198121, 67.81...   \n",
      "6  [107.12981125367459, -81.3102683906494, 68.975...   \n",
      "7  [107.7012390104641, -81.6418443459811, 69.3910...   \n",
      "8  [108.06151985814759, -82.02115475685893, 70.10...   \n",
      "\n",
      "                                          chi_in_MHz    χr MHz      gr MHz  \n",
      "1  [-3.810201304704236, -18.694204087958646, -9.7...  3.810201  102.845763  \n",
      "2  [-3.575143906278386, -15.48527763225972, -5.54...  3.575144  107.770931  \n",
      "3  [-2.888576221585544, -10.445672739152624, -4.2...  2.888576  105.548153  \n",
      "4  [-2.5743348275854756, -8.277915423269258, -3.6...  2.574335  104.905944  \n",
      "5  [-2.415933356695892, -7.106373744825816, -3.16...  2.415933  106.338976  \n",
      "6  [-2.301822505955287, -6.324281107517073, -2.95...  2.301823  107.129811  \n",
      "7  [-2.2529865943189042, -6.001303111687617, -2.8...  2.252987  107.701239  \n",
      "8  [-2.1960704619822486, -5.704387588554083, -2.7...  2.196070  108.061520  }\n",
      "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
      "bus1_connector_pad_Q1                  50.68289               -0.45605   \n",
      "bus2_connector_pad_Q1                  -0.45605               54.98763   \n",
      "ground_main_plane                     -34.03296              -36.27948   \n",
      "pad_bot_Q1                             -1.61149              -14.25514   \n",
      "pad_top_Q1                            -13.44472               -1.90696   \n",
      "readout_connector_pad_Q1               -0.21628               -1.03969   \n",
      "\n",
      "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
      "bus1_connector_pad_Q1             -34.03296    -1.61149   -13.44472   \n",
      "bus2_connector_pad_Q1             -36.27948   -14.25514    -1.90696   \n",
      "ground_main_plane                 235.25834   -30.65801   -37.24496   \n",
      "pad_bot_Q1                        -30.65801   104.10961   -36.17944   \n",
      "pad_top_Q1                        -37.24496   -36.17944    93.77409   \n",
      "readout_connector_pad_Q1          -37.12178   -19.15111    -2.31960   \n",
      "\n",
      "                          readout_connector_pad_Q1  \n",
      "bus1_connector_pad_Q1                     -0.21628  \n",
      "bus2_connector_pad_Q1                     -1.03969  \n",
      "ground_main_plane                        -37.12178  \n",
      "pad_bot_Q1                               -19.15111  \n",
      "pad_top_Q1                                -2.31960  \n",
      "readout_connector_pad_Q1                  60.98654  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4147670e-14, -3.9492000e-16, -3.1041150e-14, -1.4496900e-15,\n",
      "        -1.0486820e-14, -1.7218000e-16],\n",
      "       [-3.9492000e-16,  4.6459810e-14, -3.2923590e-14, -1.1642650e-14,\n",
      "        -4.3180000e-16, -3.3660000e-16],\n",
      "       [-3.1041150e-14, -3.2923590e-14,  2.3575625e-13, -2.7950670e-14,\n",
      "        -3.5372900e-14, -3.4016130e-14],\n",
      "       [-1.4496900e-15, -1.1642650e-14, -2.7950670e-14,  8.8123550e-14,\n",
      "        -2.9463910e-14, -1.5955250e-14],\n",
      "       [-1.0486820e-14, -4.3180000e-16, -3.5372900e-14, -2.9463910e-14,\n",
      "         7.9772790e-14, -2.0327400e-15],\n",
      "       [-1.7218000e-16, -3.3660000e-16, -3.4016130e-14, -1.5955250e-14,\n",
      "        -2.0327400e-15,  5.3205750e-14]]), 2: array([[ 4.5639150e-14, -4.5303000e-16, -3.0625850e-14, -1.4292000e-15,\n",
      "        -1.2108900e-14, -2.1362000e-16],\n",
      "       [-4.5303000e-16,  4.9745810e-14, -3.3732750e-14, -1.1903450e-14,\n",
      "        -1.7305600e-15, -1.0127300e-15],\n",
      "       [-3.0625850e-14, -3.3732750e-14,  2.3301395e-13, -2.9685080e-14,\n",
      "        -3.6138470e-14, -3.4679760e-14],\n",
      "       [-1.4292000e-15, -1.1903450e-14, -2.9685080e-14,  9.1678400e-14,\n",
      "        -3.0003290e-14, -1.6686200e-14],\n",
      "       [-1.2108900e-14, -1.7305600e-15, -3.6138470e-14, -3.0003290e-14,\n",
      "         8.4254420e-14, -1.9635700e-15],\n",
      "       [-2.1362000e-16, -1.0127300e-15, -3.4679760e-14, -1.6686200e-14,\n",
      "        -1.9635700e-15,  5.5569040e-14]]), 3: array([[ 4.8119690e-14, -4.5472000e-16, -3.2461830e-14, -1.5757300e-15,\n",
      "        -1.2521480e-14, -2.1691000e-16],\n",
      "       [-4.5472000e-16,  5.1630230e-14, -3.4489740e-14, -1.2800260e-14,\n",
      "        -1.8607800e-15, -1.0098100e-15],\n",
      "       [-3.2461830e-14, -3.4489740e-14,  2.3061805e-13, -3.0084020e-14,\n",
      "        -3.6128450e-14, -3.5501860e-14],\n",
      "       [-1.5757300e-15, -1.2800260e-14, -3.0084020e-14,  9.5969310e-14,\n",
      "        -3.1906680e-14, -1.7425530e-14],\n",
      "       [-1.2521480e-14, -1.8607800e-15, -3.6128450e-14, -3.1906680e-14,\n",
      "         8.7205650e-14, -2.2600600e-15],\n",
      "       [-2.1691000e-16, -1.0098100e-15, -3.5501860e-14, -1.7425530e-14,\n",
      "        -2.2600600e-15,  5.7518970e-14]]), 4: array([[ 4.8610720e-14, -4.5836000e-16, -3.2749720e-14, -1.5978600e-15,\n",
      "        -1.2682000e-14, -2.1824000e-16],\n",
      "       [-4.5836000e-16,  5.2578160e-14, -3.4972530e-14, -1.3197170e-14,\n",
      "        -1.8861900e-15, -1.0344000e-15],\n",
      "       [-3.2749720e-14, -3.4972530e-14,  2.3166021e-13, -3.0284290e-14,\n",
      "        -3.6528300e-14, -3.5808440e-14],\n",
      "       [-1.5978600e-15, -1.3197170e-14, -3.0284290e-14,  9.8250440e-14,\n",
      "        -3.3240560e-14, -1.7712820e-14],\n",
      "       [-1.2682000e-14, -1.8861900e-15, -3.6528300e-14, -3.3240560e-14,\n",
      "         8.9216690e-14, -2.2857800e-15],\n",
      "       [-2.1824000e-16, -1.0344000e-15, -3.5808440e-14, -1.7712820e-14,\n",
      "        -2.2857800e-15,  5.8182900e-14]]), 5: array([[ 4.9363840e-14, -4.5301000e-16, -3.3226850e-14, -1.5928200e-15,\n",
      "        -1.2970540e-14, -2.1557000e-16],\n",
      "       [-4.5301000e-16,  5.3141140e-14, -3.5279460e-14, -1.3497480e-14,\n",
      "        -1.8826900e-15, -1.0073100e-15],\n",
      "       [-3.3226850e-14, -3.5279460e-14,  2.3243426e-13, -3.0607630e-14,\n",
      "        -3.6532310e-14, -3.6209200e-14],\n",
      "       [-1.5928200e-15, -1.3497480e-14, -3.0607630e-14,  1.0081831e-13,\n",
      "        -3.4478550e-14, -1.8382560e-14],\n",
      "       [-1.2970540e-14, -1.8826900e-15, -3.6532310e-14, -3.4478550e-14,\n",
      "         9.0775280e-14, -2.2863000e-15],\n",
      "       [-2.1557000e-16, -1.0073100e-15, -3.6209200e-14, -1.8382560e-14,\n",
      "        -2.2863000e-15,  5.9218980e-14]]), 6: array([[ 4.9985210e-14, -4.5704000e-16, -3.3616870e-14, -1.6030800e-15,\n",
      "        -1.3178630e-14, -2.1615000e-16],\n",
      "       [-4.5704000e-16,  5.3973980e-14, -3.5687360e-14, -1.3866960e-14,\n",
      "        -1.9026000e-15, -1.0226000e-15],\n",
      "       [-3.3616870e-14, -3.5687360e-14,  2.3349389e-13, -3.0591980e-14,\n",
      "        -3.6927510e-14, -3.6485030e-14],\n",
      "       [-1.6030800e-15, -1.3866960e-14, -3.0591980e-14,  1.0237336e-13,\n",
      "        -3.5303540e-14, -1.8736620e-14],\n",
      "       [-1.3178630e-14, -1.9026000e-15, -3.6927510e-14, -3.5303540e-14,\n",
      "         9.2276360e-14, -2.3091300e-15],\n",
      "       [-2.1615000e-16, -1.0226000e-15, -3.6485030e-14, -1.8736620e-14,\n",
      "        -2.3091300e-15,  5.9894930e-14]]), 7: array([[ 5.0248640e-14, -4.5663000e-16, -3.3742160e-14, -1.6131600e-15,\n",
      "        -1.3298120e-14, -2.1674000e-16],\n",
      "       [-4.5663000e-16,  5.4536010e-14, -3.6050740e-14, -1.4045840e-14,\n",
      "        -1.9002700e-15, -1.0348200e-15],\n",
      "       [-3.3742160e-14, -3.6050740e-14,  2.3428136e-13, -3.0627100e-14,\n",
      "        -3.7020200e-14, -3.6890900e-14],\n",
      "       [-1.6131600e-15, -1.4045840e-14, -3.0627100e-14,  1.0327317e-13,\n",
      "        -3.5754980e-14, -1.8974920e-14],\n",
      "       [-1.3298120e-14, -1.9002700e-15, -3.7020200e-14, -3.5754980e-14,\n",
      "         9.2955270e-14, -2.3092000e-15],\n",
      "       [-2.1674000e-16, -1.0348200e-15, -3.6890900e-14, -1.8974920e-14,\n",
      "        -2.3092000e-15,  6.0565440e-14]]), 8: array([[ 5.0682890e-14, -4.5605000e-16, -3.4032960e-14, -1.6114900e-15,\n",
      "        -1.3444720e-14, -2.1628000e-16],\n",
      "       [-4.5605000e-16,  5.4987630e-14, -3.6279480e-14, -1.4255140e-14,\n",
      "        -1.9069600e-15, -1.0396900e-15],\n",
      "       [-3.4032960e-14, -3.6279480e-14,  2.3525834e-13, -3.0658010e-14,\n",
      "        -3.7244960e-14, -3.7121780e-14],\n",
      "       [-1.6114900e-15, -1.4255140e-14, -3.0658010e-14,  1.0410961e-13,\n",
      "        -3.6179440e-14, -1.9151110e-14],\n",
      "       [-1.3444720e-14, -1.9069600e-15, -3.7244960e-14, -3.6179440e-14,\n",
      "         9.3774090e-14, -2.3196000e-15],\n",
      "       [-2.1628000e-16, -1.0396900e-15, -3.7121780e-14, -1.9151110e-14,\n",
      "        -2.3196000e-15,  6.0986540e-14]])}}\n",
      "\n",
      "key=30um\n",
      "option_name['30um']['option_name']=pad_gap\n",
      "variables={'lumped_oscillator': {'fQ': 5.462896549506973, 'EC': 307.6008059702114, 'EJ': 13.616300010297985, 'alpha': -357.2874591478059, 'dispersion': 33.47434948539734, 'gbus': array([116.21339556, -87.83512791,  75.52064749]), 'chi_in_MHz': array([ -3.37823347, -11.51952754,  -5.08308206])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha dispersion  \\\n",
      "1  5.830122  353.625282  13.6163 -416.708817  113.11692   \n",
      "2  5.745499  342.689918  13.6163 -402.449428   86.65132   \n",
      "3  5.652966  330.959668  13.6163 -387.251884   64.14433   \n",
      "4  5.600191  324.374985  13.6163 -378.765066  53.788833   \n",
      "5  5.539417  316.886639  13.6163 -369.151877  43.731781   \n",
      "6  5.501965  312.321897  13.6163 -363.311786   38.40525   \n",
      "7  5.476807  309.277112  13.6163 -359.424666  35.161001   \n",
      "8  5.462897  307.600806  13.6163 -357.287459  33.474349   \n",
      "\n",
      "                                                gbus  \\\n",
      "1  [109.02524713531591, -74.94480873602234, 76.41...   \n",
      "2  [112.91991484159519, -82.54641088589537, 69.19...   \n",
      "3  [112.00253623300011, -84.53126333113148, 72.24...   \n",
      "4  [111.7129552679439, -84.54971279864021, 73.324...   \n",
      "5  [113.80880482565388, -85.29604218811512, 73.28...   \n",
      "6  [115.01096521384784, -86.429109644363, 74.2469...   \n",
      "7  [115.76279818788643, -87.36761701089993, 74.76...   \n",
      "8  [116.21339555843822, -87.83512791363434, 75.52...   \n",
      "\n",
      "                                          chi_in_MHz    χr MHz      gr MHz  \n",
      "1  [-5.399387143023984, -47.010676103849015, -16....  5.399387  109.025247  \n",
      "2  [-5.002679690889769, -32.8443439546258, -9.922...  5.002680  112.919915  \n",
      "3  [-4.221519632060214, -21.76025416309242, -7.93...  4.221520  112.002536  \n",
      "4  [-3.8586174786699887, -17.438517118939547, -6....  3.858617  111.712955  \n",
      "5  [-3.640928569155065, -14.097167414381117, -5.8...  3.640929  113.808805  \n",
      "6  [-3.510312154331424, -12.695294731236526, -5.4...  3.510312  115.010965  \n",
      "7  [-3.4230978902406988, -11.925426143748691, -5....  3.423098  115.762798  \n",
      "8  [-3.378233467896828, -11.519527540166955, -5.0...  3.378233  116.213396  }\n",
      "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
      "bus1_connector_pad_Q1                  50.59475               -0.42373   \n",
      "bus2_connector_pad_Q1                  -0.42373               54.94375   \n",
      "ground_main_plane                     -34.00537              -36.29163   \n",
      "pad_bot_Q1                             -1.54779              -14.31897   \n",
      "pad_top_Q1                            -13.49320               -1.83808   \n",
      "readout_connector_pad_Q1               -0.20664               -1.03145   \n",
      "\n",
      "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
      "bus1_connector_pad_Q1             -34.00537    -1.54779   -13.49320   \n",
      "bus2_connector_pad_Q1             -36.29163   -14.31897    -1.83808   \n",
      "ground_main_plane                 237.02640   -31.52366   -38.22709   \n",
      "pad_bot_Q1                        -31.52366    99.53492   -30.61516   \n",
      "pad_top_Q1                        -38.22709   -30.61516    89.15073   \n",
      "readout_connector_pad_Q1          -37.11942   -19.21035    -2.22435   \n",
      "\n",
      "                          readout_connector_pad_Q1  \n",
      "bus1_connector_pad_Q1                     -0.20664  \n",
      "bus2_connector_pad_Q1                     -1.03145  \n",
      "ground_main_plane                        -37.11942  \n",
      "pad_bot_Q1                               -19.21035  \n",
      "pad_top_Q1                                -2.22435  \n",
      "readout_connector_pad_Q1                  60.91761  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4164070e-14, -3.5989000e-16, -3.1093760e-14, -1.3814300e-15,\n",
      "        -1.0566140e-14, -1.6608000e-16],\n",
      "       [-3.5989000e-16,  4.6488900e-14, -3.2942860e-14, -1.1565090e-14,\n",
      "        -5.7897000e-16, -3.1986000e-16],\n",
      "       [-3.1093760e-14, -3.2942860e-14,  2.3763876e-13, -2.8915460e-14,\n",
      "        -3.6226730e-14, -3.4078370e-14],\n",
      "       [-1.3814300e-15, -1.1565090e-14, -2.8915460e-14,  8.5774560e-14,\n",
      "        -2.6142050e-14, -1.6056500e-14],\n",
      "       [-1.0566140e-14, -5.7897000e-16, -3.6226730e-14, -2.6142050e-14,\n",
      "         7.7480320e-14, -1.9372900e-15],\n",
      "       [-1.6608000e-16, -3.1986000e-16, -3.4078370e-14, -1.6056500e-14,\n",
      "        -1.9372900e-15,  5.3242550e-14]]), 2: array([[ 4.6325230e-14, -4.2047000e-16, -3.1584610e-14, -1.4162100e-15,\n",
      "        -1.1900660e-14, -2.0238000e-16],\n",
      "       [-4.2047000e-16,  4.9779490e-14, -3.3812720e-14, -1.1965030e-14,\n",
      "        -1.6715600e-15, -1.0030400e-15],\n",
      "       [-3.1584610e-14, -3.3812720e-14,  2.3584731e-13, -3.0625210e-14,\n",
      "        -3.6991100e-14, -3.4724940e-14],\n",
      "       [-1.4162100e-15, -1.1965030e-14, -3.0625210e-14,  8.9142120e-14,\n",
      "        -2.6367590e-14, -1.6742180e-14],\n",
      "       [-1.1900660e-14, -1.6715600e-15, -3.6991100e-14, -2.6367590e-14,\n",
      "         8.1202660e-14, -1.9176400e-15],\n",
      "       [-2.0238000e-16, -1.0030400e-15, -3.4724940e-14, -1.6742180e-14,\n",
      "        -1.9176400e-15,  5.5596600e-14]]), 3: array([[ 4.781116e-14, -4.224900e-16, -3.238145e-14, -1.508140e-15,\n",
      "        -1.242209e-14, -2.067700e-16],\n",
      "       [-4.224900e-16,  5.170372e-14, -3.449845e-14, -1.299176e-14,\n",
      "        -1.795240e-15, -1.000140e-15],\n",
      "       [-3.238145e-14, -3.449845e-14,  2.328565e-13, -3.103545e-14,\n",
      "        -3.708001e-14, -3.567206e-14],\n",
      "       [-1.508140e-15, -1.299176e-14, -3.103545e-14,  9.285160e-14,\n",
      "        -2.759688e-14, -1.751171e-14],\n",
      "       [-1.242209e-14, -1.795240e-15, -3.708001e-14, -2.759688e-14,\n",
      "         8.360249e-14, -2.166200e-15],\n",
      "       [-2.067700e-16, -1.000140e-15, -3.567206e-14, -1.751171e-14,\n",
      "        -2.166200e-15,  5.763952e-14]]), 4: array([[ 4.8484680e-14, -4.2765000e-16, -3.2827670e-14, -1.5419100e-15,\n",
      "        -1.2581860e-14, -2.0936000e-16],\n",
      "       [-4.2765000e-16,  5.2746780e-14, -3.5034120e-14, -1.3418960e-14,\n",
      "        -1.8169100e-15, -1.0270100e-15],\n",
      "       [-3.2827670e-14, -3.5034120e-14,  2.3360505e-13, -3.1226640e-14,\n",
      "        -3.7280880e-14, -3.5886480e-14],\n",
      "       [-1.5419100e-15, -1.3418960e-14, -3.1226640e-14,  9.4740220e-14,\n",
      "        -2.8469770e-14, -1.7808840e-14],\n",
      "       [-1.2581860e-14, -1.8169100e-15, -3.7280880e-14, -2.8469770e-14,\n",
      "         8.4958130e-14, -2.1791000e-15],\n",
      "       [-2.0936000e-16, -1.0270100e-15, -3.5886480e-14, -1.7808840e-14,\n",
      "        -2.1791000e-15,  5.8221940e-14]]), 5: array([[ 4.8928030e-14, -4.2235000e-16, -3.3016070e-14, -1.5386100e-15,\n",
      "        -1.2845170e-14, -2.0677000e-16],\n",
      "       [-4.2235000e-16,  5.3069610e-14, -3.5143740e-14, -1.3675150e-14,\n",
      "        -1.8136800e-15, -9.9928000e-16],\n",
      "       [-3.3016070e-14, -3.5143740e-14,  2.3401567e-13, -3.1505580e-14,\n",
      "        -3.7644370e-14, -3.6157100e-14],\n",
      "       [-1.5386100e-15, -1.3675150e-14, -3.1505580e-14,  9.6955680e-14,\n",
      "        -2.9440700e-14, -1.8481020e-14],\n",
      "       [-1.2845170e-14, -1.8136800e-15, -3.7644370e-14, -2.9440700e-14,\n",
      "         8.6629220e-14, -2.1968600e-15],\n",
      "       [-2.0677000e-16, -9.9928000e-16, -3.6157100e-14, -1.8481020e-14,\n",
      "        -2.1968600e-15,  5.9144960e-14]]), 6: array([[ 4.9744140e-14, -4.2602000e-16, -3.3507700e-14, -1.5477100e-15,\n",
      "        -1.3151570e-14, -2.0706000e-16],\n",
      "       [-4.2602000e-16,  5.3990280e-14, -3.5717330e-14, -1.3970180e-14,\n",
      "        -1.8331900e-15, -1.0150200e-15],\n",
      "       [-3.3507700e-14, -3.5717330e-14,  2.3538074e-13, -3.1492990e-14,\n",
      "        -3.7927300e-14, -3.6520570e-14],\n",
      "       [-1.5477100e-15, -1.3970180e-14, -3.1492990e-14,  9.8174080e-14,\n",
      "        -3.0017040e-14, -1.8830100e-14],\n",
      "       [-1.3151570e-14, -1.8331900e-15, -3.7927300e-14, -3.0017040e-14,\n",
      "         8.7842500e-14, -2.2091100e-15],\n",
      "       [-2.0706000e-16, -1.0150200e-15, -3.6520570e-14, -1.8830100e-14,\n",
      "        -2.2091100e-15,  5.9890010e-14]]), 7: array([[ 5.0304040e-14, -4.2385000e-16, -3.3825400e-14, -1.5470800e-15,\n",
      "        -1.3383390e-14, -2.0684000e-16],\n",
      "       [-4.2385000e-16,  5.4562730e-14, -3.6109810e-14, -1.4131330e-14,\n",
      "        -1.8331700e-15, -1.0255300e-15],\n",
      "       [-3.3825400e-14, -3.6109810e-14,  2.3631736e-13, -3.1479700e-14,\n",
      "        -3.8084980e-14, -3.6914650e-14],\n",
      "       [-1.5470800e-15, -1.4131330e-14, -3.1479700e-14,  9.8958140e-14,\n",
      "        -3.0432440e-14, -1.9054770e-14],\n",
      "       [-1.3383390e-14, -1.8331700e-15, -3.8084980e-14, -3.0432440e-14,\n",
      "         8.8685850e-14, -2.2178100e-15],\n",
      "       [-2.0684000e-16, -1.0255300e-15, -3.6914650e-14, -1.9054770e-14,\n",
      "        -2.2178100e-15,  6.0541990e-14]]), 8: array([[ 5.059475e-14, -4.237300e-16, -3.400537e-14, -1.547790e-15,\n",
      "        -1.349320e-14, -2.066400e-16],\n",
      "       [-4.237300e-16,  5.494375e-14, -3.629163e-14, -1.431897e-14,\n",
      "        -1.838080e-15, -1.031450e-15],\n",
      "       [-3.400537e-14, -3.629163e-14,  2.370264e-13, -3.152366e-14,\n",
      "        -3.822709e-14, -3.711942e-14],\n",
      "       [-1.547790e-15, -1.431897e-14, -3.152366e-14,  9.953492e-14,\n",
      "        -3.061516e-14, -1.921035e-14],\n",
      "       [-1.349320e-14, -1.838080e-15, -3.822709e-14, -3.061516e-14,\n",
      "         8.915073e-14, -2.224350e-15],\n",
      "       [-2.066400e-16, -1.031450e-15, -3.711942e-14, -1.921035e-14,\n",
      "        -2.224350e-15,  6.091761e-14]])}}\n",
      "\n",
      "key=40um\n",
      "option_name['40um']['option_name']=pad_gap\n",
      "variables={'lumped_oscillator': {'fQ': 5.603196288676893, 'EC': 324.7478590723875, 'EJ': 13.616300010297985, 'alpha': -379.24480934899753, 'dispersion': 54.335718358039856, 'gbus': array([122.75192426, -92.44407275,  79.74179062]), 'chi_in_MHz': array([ -4.68116172, -21.09933964,  -8.31554677])}, 'lumped_oscillator_all':          fQ          EC       EJ       alpha  dispersion  \\\n",
      "1  5.942275   368.42715  13.6163 -436.152747  159.132464   \n",
      "2  5.860089  357.545759  13.6163 -421.842741  124.079999   \n",
      "3   5.77753   346.80562  13.6163 -407.805744   95.939037   \n",
      "4  5.737369  341.649788  13.6163 -401.097753   84.424981   \n",
      "5  5.684596  334.942753  13.6163 -392.400966   71.169458   \n",
      "6  5.644678  329.920535  13.6163 -385.910471   62.408863   \n",
      "7  5.619535  326.779545  13.6163 -381.860572   57.396903   \n",
      "8  5.603196  324.747859  13.6163 -379.244809   54.335718   \n",
      "\n",
      "                                                gbus  \\\n",
      "1  [114.42468061343125, -78.23514734277781, 77.94...   \n",
      "2  [118.14507620202656, -86.1504566631403, 72.301...   \n",
      "3  [117.78733127044518, -88.41648711373728, 75.26...   \n",
      "4  [117.84135564008048, -88.71652047403923, 76.68...   \n",
      "5  [120.25622025638054, -89.93776777738493, 76.86...   \n",
      "6  [121.56948305978523, -90.94565691616705, 78.44...   \n",
      "7  [122.23628316058053, -92.09397182340028, 78.79...   \n",
      "8  [122.75192426481861, -92.44407275077926, 79.74...   \n",
      "\n",
      "                                          chi_in_MHz    χr MHz      gr MHz  \n",
      "1  [-7.298600527505494, -187.3189810121671, -29.6...  7.298601  114.424681  \n",
      "2  [-6.688603442516156, -79.71703699688189, -17.0...  6.688603  118.145076  \n",
      "3  [-5.749406339167914, -45.519940778626875, -13....  5.749406  117.787331  \n",
      "4  [-5.3738345109544365, -36.26780028757956, -11....  5.373835  117.841356  \n",
      "5  [-5.12495836977476, -28.483809227385823, -9.94...  5.124958  120.256220  \n",
      "6  [-4.907144357646663, -24.287140896629243, -9.1...  4.907144  121.569483  \n",
      "7  [-4.764435821161131, -22.38230347227866, -8.52...  4.764436  122.236283  \n",
      "8  [-4.681161717420851, -21.099339639730726, -8.3...  4.681162  122.751924  }\n",
      "sim_variables={'sim_setup_name': 'sweeper_q3d_setup', 'cap_matrix':                           bus1_connector_pad_Q1  bus2_connector_pad_Q1  \\\n",
      "bus1_connector_pad_Q1                  50.64383               -0.39535   \n",
      "bus2_connector_pad_Q1                  -0.39535               55.01423   \n",
      "ground_main_plane                     -34.09242              -36.42933   \n",
      "pad_bot_Q1                             -1.48913              -14.37074   \n",
      "pad_top_Q1                            -13.55646               -1.76745   \n",
      "readout_connector_pad_Q1               -0.19817               -1.02182   \n",
      "\n",
      "                          ground_main_plane  pad_bot_Q1  pad_top_Q1  \\\n",
      "bus1_connector_pad_Q1             -34.09242    -1.48913   -13.55646   \n",
      "bus2_connector_pad_Q1             -36.42933   -14.37074    -1.76745   \n",
      "ground_main_plane                 239.00028   -32.35105   -39.13830   \n",
      "pad_bot_Q1                        -32.35105    96.74559   -26.86254   \n",
      "pad_top_Q1                        -39.13830   -26.86254    86.26515   \n",
      "readout_connector_pad_Q1          -37.20723   -19.29195    -2.13165   \n",
      "\n",
      "                          readout_connector_pad_Q1  \n",
      "bus1_connector_pad_Q1                     -0.19817  \n",
      "bus2_connector_pad_Q1                     -1.02182  \n",
      "ground_main_plane                        -37.20723  \n",
      "pad_bot_Q1                               -19.29195  \n",
      "pad_top_Q1                                -2.13165  \n",
      "readout_connector_pad_Q1                  60.96517  , 'units': 'fF', 'cap_all_passes': {1: array([[ 4.4182950e-14, -3.2910000e-16, -3.1155100e-14, -1.3180500e-15,\n",
      "        -1.0630010e-14, -1.6021000e-16],\n",
      "       [-3.2910000e-16,  4.6523900e-14, -3.2982350e-14, -1.1519720e-14,\n",
      "        -6.7360000e-16, -3.0400000e-16],\n",
      "       [-3.1155100e-14, -3.2982350e-14,  2.3943871e-13, -2.9786690e-14,\n",
      "        -3.7040360e-14, -3.4155240e-14],\n",
      "       [-1.3180500e-15, -1.1519720e-14, -2.9786690e-14,  8.4031320e-14,\n",
      "        -2.3505740e-14, -1.6139880e-14],\n",
      "       [-1.0630010e-14, -6.7360000e-16, -3.7040360e-14, -2.3505740e-14,\n",
      "         7.5765500e-14, -1.8454500e-15],\n",
      "       [-1.6021000e-16, -3.0400000e-16, -3.4155240e-14, -1.6139880e-14,\n",
      "        -1.8454500e-15,  5.3281010e-14]]), 2: array([[ 4.5616390e-14, -3.9686000e-16, -3.0842440e-14, -1.3883800e-15,\n",
      "        -1.1984460e-14, -1.9837000e-16],\n",
      "       [-3.9686000e-16,  4.9820940e-14, -3.3872680e-14, -1.2002070e-14,\n",
      "        -1.6197500e-15, -1.0311400e-15],\n",
      "       [-3.0842440e-14, -3.3872680e-14,  2.3684909e-13, -3.1346120e-14,\n",
      "        -3.7795590e-14, -3.4975700e-14],\n",
      "       [-1.3883800e-15, -1.2002070e-14, -3.1346120e-14,  8.7279510e-14,\n",
      "        -2.3604360e-14, -1.6865180e-14],\n",
      "       [-1.1984460e-14, -1.6197500e-15, -3.7795590e-14, -2.3604360e-14,\n",
      "         7.9292480e-14, -1.8851600e-15],\n",
      "       [-1.9837000e-16, -1.0311400e-15, -3.4975700e-14, -1.6865180e-14,\n",
      "        -1.8851600e-15,  5.5964780e-14]]), 3: array([[ 4.774243e-14, -3.945600e-16, -3.234879e-14, -1.454270e-15,\n",
      "        -1.247981e-14, -1.988300e-16],\n",
      "       [-3.945600e-16,  5.157865e-14, -3.453043e-14, -1.294261e-14,\n",
      "        -1.728270e-15, -9.924400e-16],\n",
      "       [-3.234879e-14, -3.453043e-14,  2.344610e-13, -3.183040e-14,\n",
      "        -3.793489e-14, -3.571656e-14],\n",
      "       [-1.454270e-15, -1.294261e-14, -3.183040e-14,  9.062371e-14,\n",
      "        -2.453722e-14, -1.759460e-14],\n",
      "       [-1.247981e-14, -1.728270e-15, -3.793489e-14, -2.453722e-14,\n",
      "         8.135669e-14, -2.079910e-15],\n",
      "       [-1.988300e-16, -9.924400e-16, -3.571656e-14, -1.759460e-14,\n",
      "        -2.079910e-15,  5.766013e-14]]), 4: array([[ 4.8375450e-14, -3.9882000e-16, -3.2753100e-14, -1.4809000e-15,\n",
      "        -1.2649340e-14, -2.0097000e-16],\n",
      "       [-3.9882000e-16,  5.2595680e-14, -3.5087220e-14, -1.3334920e-14,\n",
      "        -1.7464400e-15, -1.0145900e-15],\n",
      "       [-3.2753100e-14, -3.5087220e-14,  2.3528881e-13, -3.1988810e-14,\n",
      "        -3.8217180e-14, -3.5959440e-14],\n",
      "       [-1.4809000e-15, -1.3334920e-14, -3.1988810e-14,  9.2036790e-14,\n",
      "        -2.5065330e-14, -1.7840350e-14],\n",
      "       [-1.2649340e-14, -1.7464400e-15, -3.8217180e-14, -2.5065330e-14,\n",
      "         8.2460870e-14, -2.0930200e-15],\n",
      "       [-2.0097000e-16, -1.0145900e-15, -3.5959440e-14, -1.7840350e-14,\n",
      "        -2.0930200e-15,  5.8211970e-14]]), 5: array([[ 4.9096690e-14, -3.9415000e-16, -3.3227640e-14, -1.4835300e-15,\n",
      "        -1.2897560e-14, -1.9845000e-16],\n",
      "       [-3.9415000e-16,  5.3034830e-14, -3.5258960e-14, -1.3647410e-14,\n",
      "        -1.7376000e-15, -9.8836000e-16],\n",
      "       [-3.3227640e-14, -3.5258960e-14,  2.3597375e-13, -3.2372580e-14,\n",
      "        -3.8395660e-14, -3.6226080e-14],\n",
      "       [-1.4835300e-15, -1.3647410e-14, -3.2372580e-14,  9.4223890e-14,\n",
      "        -2.5781500e-14, -1.8561290e-14],\n",
      "       [-1.2897560e-14, -1.7376000e-15, -3.8395660e-14, -2.5781500e-14,\n",
      "         8.3649800e-14, -2.0921300e-15],\n",
      "       [-1.9845000e-16, -9.8836000e-16, -3.6226080e-14, -1.8561290e-14,\n",
      "        -2.0921300e-15,  5.9163860e-14]]), 6: array([[ 4.982926e-14, -3.967100e-16, -3.363908e-14, -1.487400e-15,\n",
      "        -1.320745e-14, -1.987400e-16],\n",
      "       [-3.967100e-16,  5.400163e-14, -3.580562e-14, -1.401215e-14,\n",
      "        -1.762220e-15, -1.003590e-15],\n",
      "       [-3.363908e-14, -3.580562e-14,  2.372922e-13, -3.228836e-14,\n",
      "        -3.884504e-14, -3.659899e-14],\n",
      "       [-1.487400e-15, -1.401215e-14, -3.228836e-14,  9.536075e-14,\n",
      "        -2.629024e-14, -1.890722e-14],\n",
      "       [-1.320745e-14, -1.762220e-15, -3.884504e-14, -2.629024e-14,\n",
      "         8.499589e-14, -2.120500e-15],\n",
      "       [-1.987400e-16, -1.003590e-15, -3.659899e-14, -1.890722e-14,\n",
      "        -2.120500e-15,  5.993182e-14]]), 7: array([[ 5.0244360e-14, -3.9629000e-16, -3.3810070e-14, -1.4880600e-15,\n",
      "        -1.3444540e-14, -1.9878000e-16],\n",
      "       [-3.9629000e-16,  5.4549730e-14, -3.6174730e-14, -1.4169490e-14,\n",
      "        -1.7662900e-15, -1.0135700e-15],\n",
      "       [-3.3810070e-14, -3.6174730e-14,  2.3804178e-13, -3.2340100e-14,\n",
      "        -3.8915950e-14, -3.6999550e-14],\n",
      "       [-1.4880600e-15, -1.4169490e-14, -3.2340100e-14,  9.6209560e-14,\n",
      "        -2.6668040e-14, -1.9141080e-14],\n",
      "       [-1.3444540e-14, -1.7662900e-15, -3.8915950e-14, -2.6668040e-14,\n",
      "         8.5726250e-14, -2.1270600e-15],\n",
      "       [-1.9878000e-16, -1.0135700e-15, -3.6999550e-14, -1.9141080e-14,\n",
      "        -2.1270600e-15,  6.0594280e-14]]), 8: array([[ 5.0643830e-14, -3.9535000e-16, -3.4092420e-14, -1.4891300e-15,\n",
      "        -1.3556460e-14, -1.9817000e-16],\n",
      "       [-3.9535000e-16,  5.5014230e-14, -3.6429330e-14, -1.4370740e-14,\n",
      "        -1.7674500e-15, -1.0218200e-15],\n",
      "       [-3.4092420e-14, -3.6429330e-14,  2.3900028e-13, -3.2351050e-14,\n",
      "        -3.9138300e-14, -3.7207230e-14],\n",
      "       [-1.4891300e-15, -1.4370740e-14, -3.2351050e-14,  9.6745590e-14,\n",
      "        -2.6862540e-14, -1.9291950e-14],\n",
      "       [-1.3556460e-14, -1.7674500e-15, -3.9138300e-14, -2.6862540e-14,\n",
      "         8.6265150e-14, -2.1316500e-15],\n",
      "       [-1.9817000e-16, -1.0218200e-15, -3.7207230e-14, -1.9291950e-14,\n",
      "        -2.1316500e-15,  6.0965170e-14]])}}\n"
     ]
    }
   ],
   "source": [
    "if return_code ==0:\n",
    "    print(f'{sweep_data.keys()} \\n')\n",
    "    for key in sweep_data.keys():\n",
    "        print(f'\\nkey={key}')\n",
    "        \n",
    "        option_name = sweep_data[key]['option_name']\n",
    "        print(f'option_name[\\'{key}\\'][\\'option_name\\']={option_name}')\n",
    "        \n",
    "    \n",
    "        variables = sweep_data[key]['variables']\n",
    "        sim_variables = sweep_data[key]['sim_variables']\n",
    "        \n",
    "        print(f'variables={variables}')\n",
    "        print(f'sim_variables={sim_variables}')\n",
    "        \n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# READ THIS BEFORE running the cell.\n",
    "This cell is to demonstrate that if you have already executed c1.sim.run(), you don't have to set up \n",
    "the environment again for c1.run_sweep().  In another words, if you don't pass updated arguments to\n",
    "c1.run_sweep(), then c1.run_sweep() looks for the previous desgin arguments. \n",
    "\n",
    "If you pass anything more than these three arguments: qcomp_name, option_name, option_sweep ..... \n",
    "Then NOTHING will be used from previous run.  \n",
    "```\n",
    "c1.sim.solution_order = 'Medium'\n",
    "c1.sim.auto_increase_solution_order = 'False'\n",
    "\n",
    "\n",
    "c1.sim.run(components=render_design_argument_qcomps,\n",
    "           open_terminations=render_design_argument_endcaps)\n",
    "```\n",
    "\n",
    "Because c1.sim.setup.run has the information from last run, this is OK.\n",
    "\n",
    "```\n",
    "sweep_data, return_code = c1.run_sweep(q1.name, \n",
    "                                        'pad_gap', \n",
    "                                        ['20um', '30um', '40um'])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1.sim.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uncomment next line if you would like to close the gui\n",
    "gui.main_window.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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